class_docstr: Dataset for NP Semantic Segmentation Model
  
      Args:
          file_path (str): read data from this local file path
  
          train_to_test_ratio (:obj:`float`): the train-to-test ration of the dataset
  
          feature_vec_dim (:obj:`int`): the size of the feature vector for each noun-phrase
docstr: Load data into dict of 'train' and 'test' datasets
  
  Returns:
      dict: dict with train set & test_set (each is dict with X and y)
docstr_tok: ["Load","data","into","dict","of","'train","'","and","'test","'","datasets","Returns",":","dict",":","dict","with","train","set","&","test_set","(","each","is","dict","with","X","and","y",")"]
code: def load_data_to_array_iterator(self):
      """
      Load data into dict of 'train' and 'test' datasets
  
      Returns:
          dict: dict with train set & test_set (each is dict with X and y)
      """
      X_train, y_train, X_test, y_test = self.load_data_from_file()
      data_set = {
          "train": {"X": X_train, "y": y_train},
          "test": {"X": X_test, "y": y_test},
      }
      return data_set
  
imports: import argparse
  import csv
  import math
  import os
  from multiprocessing import Pool
  import feature_extraction
  import numpy
  from tqdm import tqdm
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate_proxy_path
label_desc: Use image node_id 3 for calling the NpSemanticSegData obj's underlying member method code with example usage: obj.load_data_to_array_iterator() and returns: data_set